We present FoundationPose a unified foundation model for 6D object pose estimation and
tracking supporting both model-based and model-free setups. Our approach can be instantly …

We present a near real-time (10Hz) method for 6-DoF tracking of an unknown object from a
monocular RGBD video sequence, while simultaneously performing neural 3D …

Motion capture technology plays a crucial role in optimizing athletes' skills, techniques, and
strategies by providing detailed feedback on motion data. This article presents a …

Object pose estimation is a fundamental computer vision problem with broad applications in
augmented reality and robotics. Over the past decade, deep learning models, due to their …

We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects
unseen during training. At inference time, the method only assumes knowledge of (i) a …

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D
poses in cluttered scenes presenting significant challenges for model generalizability …

In this paper, we present a generalizable model-free 6-DoF object pose estimator called
Gen6D. Existing generalizable pose estimators either need the high-quality object models or …

We propose FoundPose, a model-based method for 6D pose estimation of unseen objects
from a single RGB image. The method can quickly onboard new objects using their 3D …

Test-time adaptation methods have been gaining attention recently as a practical solution for
addressing source-to-target domain gaps by gradually updating the model without requiring …

Current 6D pose estimation methods focus on handling objects that are previously trained,
which limits their applications in real dynamic world. To this end, we propose a geometry …